Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2309.11933v1
• Date: Thu, 21 Sep 2023 09:47:47 GMT
• Title: Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation
• Authors: Ping Li and Yu Zhang and Li Yuan and Xianghua Xu
• Abstract summary: We propose an end-to-end RVOS framework which treats the RVOS task as a mask sequence learning problem. To capture the object-level spatial context, we have developed the Stacked Transformer. The model finds the best matching between mask sequence and text query.
• Score: 24.814534011440877
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not explicitly model the object-level spatial context which plays an important role in locating the referred object. Therefore, we propose an end-to-end RVOS framework completely built upon transformers, termed \textit{Fully Transformer-Equipped Architecture} (FTEA), which treats the RVOS task as a mask sequence learning problem and regards all the objects in video as candidate objects. Given a video clip with a text query, the visual-textual features are yielded by encoder, while the corresponding pixel-level and word-level features are aligned in terms of semantic similarity. To capture the object-level spatial context, we have developed the Stacked Transformer, which individually characterizes the visual appearance of each candidate object, whose feature map is decoded to the binary mask sequence in order directly. Finally, the model finds the best matching between mask sequence and text query. In addition, to diversify the generated masks for candidate objects, we impose a diversity loss on the model for capturing more accurate mask of the referred object. Empirical studies have shown the superiority of the proposed method on three benchmarks, e.g., FETA achieves 45.1% and 38.7% in terms of mAP on A2D Sentences (3782 videos) and J-HMDB Sentences (928 videos), respectively; it achieves 56.6% in terms of $\mathcal{J\&F}$ on Ref-YouTube-VOS (3975 videos and 7451 objects). Particularly, compared to the best candidate method, it has a gain of 2.1% and 3.2% in terms of P$@$0.5 on the former two, respectively, while it has a gain of 2.9% in terms of $\mathcal{J}$ on the latter one.

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